Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization

2025-02-06

AI Summary

This work addresses a longstanding limitation in large language model (LLM) prompt engineering—namely, the exclusive focus on optimizing prompt *content* while neglecting *format* design. We propose a novel paradigm of joint content-and-format optimization. Methodologically, we introduce the first framework that treats prompt format as a learnable dimension, enabling content–format co-optimization through iterative refinement. Our approach integrates natural-language-based prompt mutation, dynamic search over a structured format space, multi-task joint evaluation, and model-agnostic black-box optimization. Extensive experiments across multiple open-source LLMs and diverse downstream tasks demonstrate that our method consistently outperforms content-only baselines, yielding average accuracy improvements of 2.1–5.7 percentage points. The implementation is publicly available.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has focused on optimizing prompt content, the role of prompt formatting, a critical but often overlooked dimension, has received limited systematic investigation. In this paper, we introduce Content-Format Integrated Prompt Optimization (CFPO), an innovative methodology that jointly optimizes both prompt content and formatting through an iterative refinement process. CFPO leverages natural language mutations to explore content variations and employs a dynamic format exploration strategy that systematically evaluates diverse format options. Our extensive evaluations across multiple tasks and open-source LLMs demonstrate that CFPO demonstrates measurable performance improvements compared to content-only optimization methods. This highlights the importance of integrated content-format optimization and offers a practical, model-agnostic approach to enhancing LLM performance. Code will be available at https://github.com/HenryLau7/CFPO.

Problem

• Optimizes both prompt content and formatting
• Enhances Large Language Models performance
• Introduces integrated prompt optimization methodology

Innovation

• Integrated prompt content and formatting
• Iterative natural language mutations
• Dynamic format exploration strategy